!
∀#∃%&∃∋
∃()∃∋∃)&∃∋∃∗∃&∃+,−∃.∃−∃.
∃/∃
+∃,&∃+
∃%.∃+
∃&∃0&
∃.+∃%1∃∋,∃%#∃&∃∃2∗∃
∃+∃#∃/2∃
∃∗∃)3#∃∗∃/3∃,∃+1
∃)&∃3∃,
∃+45 6−
&
7
#
%
8
&#+
,
&#∃5!4969 9:9 ; (%%∗!<:!= >
7= ,&(:2: := ?
6516
JOURNAL OF CLIMATE
VOLUME 28
Processes Controlling Tropical Tropopause Temperature and Stratospheric Water Vapor
in Climate Models
STEVEN C. HARDIMAN,* IAN A. BOUTLE,* ANDREW C. BUSHELL,* NEAL BUTCHART,* MIKE J. P. CULLEN,*
PAUL R. FIELD,* KALLI FURTADO,* JAMES C. MANNERS,* SEAN F. MILTON,* CYRIL MORCRETTE,*
FIONA M. O’CONNOR,* BEN J. SHIPWAY,* CHRIS SMITH,* DAVID N. WALTERS,* MARTIN R. WILLETT,*
KEITH D. WILLIAMS,* NIGEL WOOD,* N. LUKE ABRAHAM,1,# JAMES KEEBLE,1 AMANDA C. MAYCOCK,1,#
JOHN THUBURN,@ AND MATTHEW T. WOODHOUSE&
1
* Met Office, Exeter, Devon, United Kingdom
Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
#
National Centre for Atmospheric Science, Cambridge, United Kingdom
@
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
&
Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria, Australia
(Manuscript received 16 January 2015, in final form 19 May 2015)
ABSTRACT
A warm bias in tropical tropopause temperature is found in the Met Office Unified Model (MetUM), in
common with most models from phase 5 of CMIP (CMIP5). Key dynamical, microphysical, and radiative
processes influencing the tropical tropopause temperature and lower-stratospheric water vapor concentrations in climate models are investigated using the MetUM. A series of sensitivity experiments are run to
separate the effects of vertical advection, ice optical and microphysical properties, convection, cirrus clouds,
and atmospheric composition on simulated tropopause temperature and lower-stratospheric water vapor
concentrations in the tropics. The numerical accuracy of the vertical advection, determined in the MetUM by
the choice of interpolation and conservation schemes used, is found to be particularly important. Microphysical and radiative processes are found to influence stratospheric water vapor both through modifying the
tropical tropopause temperature and through modifying upper-tropospheric water vapor concentrations,
allowing more water vapor to be advected into the stratosphere. The representation of any of the processes
discussed can act to significantly reduce biases in tropical tropopause temperature and stratospheric water
vapor in a physical way, thereby improving climate simulations.
1. Introduction
Substantial observed variations in stratospheric water
vapor (Rosenlof et al. 2001; Solomon et al. 2010) may
have a noticeable impact on lower-stratospheric temperature (Forster and Shine 1999; Maycock et al. 2014)
and surface climate (Forster and Shine 2002). Although
the Intergovernmental Panel on Climate Change Fifth
Assessment Report concluded that variations in stratospheric water vapor were unlikely to have contributed to
the recent hiatus in the global mean temperature trend
(Flato et al. 2013), they could contribute to decadal
changes in global mean surface temperature (Solomon
Corresponding author address: Steven C. Hardiman, Met Office
Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom.
E-mail: [email protected]
DOI: 10.1175/JCLI-D-15-0075.1
Ó 2015 American Meteorological Society
et al. 2010) and directly impact the stratospheric circulation and tropospheric jet streams (Maycock et al. 2013).
As well as the direct influence of stratospheric water
vapor on radiative balance, water vapor can also have a
substantial impact on stratospheric chemistry. Increased
water vapor in the extratropical stratosphere can lead to
increased polar stratospheric cloud formation and therefore potentially enhance polar ozone depletion (Toon
et al. 1989; Solomon et al. 1986; Kirk-Davidoff et al. 1999).
These changes in stratospheric ozone can, in turn, influence surface climate and radiation (Son et al. 2008;
Roscoe and Haigh 2007; Madronich et al. 1995; McKenzie
et al. 1999; Hegglin and Shepherd 2009).
Water vapor enters the stratosphere in the tropics, upwelling from the tropical upper troposphere, such that the
concentrations of water vapor in an air parcel in the
tropical lower stratosphere are predominantly determined
15 AUGUST 2015
HARDIMAN ET AL.
6517
FIG. 1. Schematic showing different processes in the tropical tropopause layer affecting the
cold-point temperature and stratospheric water vapor concentrations. Depiction of all processes, including position of model levels, is purely schematic. Blue shading represents water
vapor concentration, decreasing throughout the TTL.
by the coldest temperatures encountered by that air parcel
along its pathway into the lower stratosphere (Mote et al.
1996; Holton and Gettelman 2001), the so-called Lagrangian
dry point (Fueglistaler et al. 2013; Zahn et al. 2014). In this
paper the zonal-mean temperature at the tropical tropopause, here referred to as the ‘‘cold point,’’ is used since the
Lagrangian dry point cannot be calculated. This cold point
is a reasonable proxy for dehydration within the tropical
tropopause layer (TTL; Fueglistaler et al. 2009) and variability in the cold point is found to explain much of the
variability in lower-stratospheric water vapor concentrations
(Fueglistaler and Haynes 2005; Gettelman et al. 2010). A
realistic simulation of both surface climate change and
stratospheric chemistry therefore relies upon climate models
having an accurate representation of tropical tropopause
temperature and stratospheric water vapor concentrations.
Climate models represent many processes that have the
potential to significantly influence the amount of water
vapor entering the stratosphere, either directly or via
changes to the tropical tropopause temperature (Fig. 1).
Cirrus clouds formed in the tropical tropopause region can
significantly reduce water vapor mixing ratios in the lower
stratosphere, ‘‘freeze drying’’ the air when the water vapor
precipitates in the form of ice particles that grow in size as
they descend (Jensen and Pfister 2004). This dehydration
occurs as a result of the slow horizontal movement of air
into this region of thin widely spread cirrus, and not just
because of the deep convection of air through this region.
The amount of dehydration occurring as a result of these
cirrus clouds, although dominated by the air temperature,
is also somewhat sensitive to the microphysical processes
controlling the ice crystal number densities, particle size
distribution (PSD), and fall speed. Horizontal motion
within the TTL can result in this dehydrated air spreading
over large areas (Holton and Gettelman 2001). Deep
convection penetrating into the TTL may either hydrate or
dehydrate the tropopause layer, depending on how much
ice is removed by precipitation and on the vertical profile
of relative humidity (Jensen et al. 2007). Radiative forcing
from greenhouse gases, particularly from ozone in the
lower stratosphere (Lacis et al. 1990), and the numerical
accuracy of the vertical advection of potential temperature
across the tropopause can also influence the cold-point
temperature, as can vertical mixing (Flannaghan and
Fueglistaler 2011, 2014). Numerical accuracy of the vertical advection of moisture can directly influence the water
vapor concentrations entering the stratosphere.
Figure 2 shows tropical temperature biases, relative to
ERA-Interim (hereinafter ERA-I; ECMWF 2011; Dee
et al. 2011), found in the state-of-the-art climate models
that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012).
Across a majority of the models, there is a similar vertical
structure in the temperature bias, consisting of a cold bias
of around 1.5 K throughout the troposphere, a warm bias of
around 2 K at the tropical tropopause, and a smaller warm
bias throughout the lower stratosphere [see also Fig. 1 of
Kim et al. (2013)]. The structure of this bias is discussed
further in section 2. The Met Office submissions to CMIP5
suffer qualitatively from the same temperature bias as do
the majority of models (Fig. 2), but the magnitude of the
warm bias at the tropical tropopause is particularly large in
these simulations (around 4 K). The main aim of this paper,
using global configurations of the Met Office Unified
Model described in section 2, is to understand the effect of
processes important for controlling the atmospheric temperature and humidity in the region of the tropical tropopause in climate models.
6518
JOURNAL OF CLIMATE
FIG. 2. Bias with respect to ERA-I in annual-mean tropical temperature area averaged from 108S to 108N for all CMIP5 models for
which data were available in the central archive. Shown are 10-yr
climatologies for the period 1990–99. Individual model biases are
shown as thin blue lines, with the multimodel mean plotted as a thick
red line. The versions of the MetUM used in CMIP5 (HadGEM2; see
Table 1) are shown as thin green lines. The gray shading indicates the
intermodel standard deviation about the multimodel mean.
A range of sensitivity experiments are carried out,
modifying each process in turn. These processes, and
their impacts, are described in detail in sections 3 and 4.
Although the quantitative results of these sensitivity
experiments may be specific to the Met Office model it is
anticipated, given the similarity of the temperature
biases across CMIP5 models (Fig. 2), that the processes
described, and the impacts they can have on the tropical
tropopause temperature and stratospheric water vapor
concentrations, will be relevant to other models, and
hence our results are of general interest. Discussions and
concluding remarks are given in section 5.
2. Model description, biases, and simulations
a. Model description
We present investigations using global simulations of
the Met Office Unified Model (MetUM), which is an
atmosphere model used for numerical weather prediction
and climate simulations in both global and limited area
VOLUME 28
configurations (Cullen 1993; Brown et al. 2012). The
MetUM simulations in this study primarily use the Global
Atmosphere 6.0 (GA6.0) configuration (Walters et al.
2015, unpublished manuscript) of the Hadley Centre
Global Environmental Model, version 3 (HadGEM3).
For details of older model versions used, see the references given in Table 1.
The model simulations are atmosphere–land-only, using prescribed sea surface temperatures and sea ice concentrations following the protocol of the Atmospheric
Model Intercomparison Project (AMIP, now an integral
part of CMIP; see Taylor et al. 2012). They have a horizontal resolution of 1.8758 (longitude) 3 1.258 (latitude),
corresponding to a resolution of approximately 135 km in
the midlatitudes. There are 85 model levels, 50 of which
are in the troposphere, with the model upper boundary at
85 km from the surface. In the TTL (;11–17 km), the
spacing between model levels ranges from 500 to 700 m.
The majority of the sensitivity experiments detailed in the
following sections use GA6.0 as their baseline and were
run for 20 years in present-day conditions (1989–2008),
with the first 10 years discarded as spinup.
Here, specific details on the model’s representation of
the processes described in Fig. 1 are provided, and the
reader can refer to Walters et al. (2015, unpublished
manuscript) for further details. The model uses a semiimplicit, semi-Lagrangian formulation to solve the
nonhydrostatic, fully compressible deep-atmosphere
equations of motion (Wood et al. 2014). For temperature and moisture the model uses variables of virtual potential temperature and mass mixing ratios of vapor and
hydrometeors. A cubic Lagrange horizontal interpolation
to the semi-Lagrangian departure points is used, while in
the vertical a cubic Hermite interpolation for potential
temperature and quintic Lagrange interpolation for the
moist variables are used. The radiation scheme is described in Edwards and Slingo (1996) and Cusack et al.
(1999). Shortwave (SW) radiative transfer uses six bands
and models interactions with water vapor (H2O), ozone
(O3), carbon dioxide (CO2), and oxygen (O2). Longwave
(LW) transfer uses nine bands and models interactions
with H2O, O3, CO2, methane (CH4), nitrous oxide (N2O),
TABLE 1. Versions of the MetUM.
Model name
Hadley Centre Coupled Model, version 3 (HadCM3)
Hadley Centre Global Environment Model, version 1 (HadGEM1)
Hadley Centre Global Environment Model, version 2 (HadGEM2)
HadGEM3, Global Atmosphere 3.0 (GA3.0) configuration
HadGEM3, Global Atmosphere 4.0 (GA4.0) configuration
HadGEM3, Global Atmosphere 6.0 (GA6.0) configuration
References
Pope et al. (2000); Collins et al. (2001)
Martin et al. (2006); Hardiman et al. (2010) (stratosphere
resolving version)
Martin et al. (2011); Jones et al. (2011)
Walters et al. (2011)
Walters et al. (2014)
Walters et al. (2015, unpublished manuscript)
15 AUGUST 2015
HARDIMAN ET AL.
FIG. 3. Bias with respect to ERA-I in annual-mean tropical
temperature area averaged from 108S to 108N for different stratosphere resolving versions of HadGEM and for HadCM3 (see Table
1). Ten-year climatologies for the period 1990–99 are used. Models
are atmosphere-only standard configurations. The thick red line
shows the difference between RICH-adjusted radiosonde temperature data and ERA-I and gives a measure of how accurate the
ERA-I assimilated temperatures are. The thick black line shows
the difference MERRA minus ERA-I and gives a measure of the
uncertainty in the value of the annual-mean tropical temperature.
chlorofluorocarbon (CFC)-11 (CCl3F), CFC-12 (CCl2F2),
and hydrofluorocarbon (HFC) 134a (CH2FCF3). The
precipitation/microphysics scheme is based on Wilson
and Ballard (1999). Large-scale clouds are modeled
through the prognostic cloud fraction and prognostic
condensate (PC2) scheme (Wilson et al. 2008) with
modifications described in Morcrette (2012). This includes water vapor, liquid, and ice cloud condensate as
well as separate variables for liquid, ice, and mixed-phase
cloud fraction. Finally, the convection scheme uses a mass
flux formulation based on Gregory and Rowntree (1990)
with various extensions to include downdrafts (Gregory
and Allen 1991) and convective momentum transport.
b. Model biases
Although this study focuses primarily on the model
temperature bias in the tropical tropopause region, it
was shown in the introduction how this bias relates to the
tropical temperature bias throughout the depth of the
troposphere and stratosphere. Figure 3 shows that all
versions of HadGEM, and indeed the older-generation
HadCM3 configuration of the MetUM (see Table 1),
have suffered qualitatively from the same temperature
bias throughout the troposphere and lower stratosphere
in the tropics as is found in the majority of the CMIP5
models. This bias is shown relative to ERA-I, but it can
be seen that qualitatively the same bias is found relative to the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Global Modeling
6519
and Assimilation Office 2011; Rienecker et al. 2011),
and that the agreement between ERA-I and MERRA is
within 61 K at most altitudes. Furthermore, ERA-I is
found to agree very well with the Radiosonde Innovation
Composite Homogenization (RICH)-adjusted radiosonde
temperature data (Haimberger et al. 2012, 2013) at
100 hPa, and so might be considered as close to the ‘‘truth’’
at this altitude as can currently be obtained. The fact that
the vertical profile of this temperature bias is qualitatively
similar across all versions of HadGEM, and across the
CMIP5 models, suggests that the causes for the bias may be
common across these models (Kim et al. 2013).
In numerical models, the cold-point temperature is
not solely responsible for the stratospheric water vapor
concentrations. Biases in tropical stratospheric water
vapor are shown in Figs. 4a and 4b for the most recent
versions of HadGEM3, and in Fig. 4c for the CMIP5
models. The biases are shown relative to MERRA
(Rienecker et al. 2011), which assimilates water vapor
data from both the Halogen Occultation Experiment
(HALOE; Russell et al. 1993) and the Aura Microwave
Limb Sounder (MLS; Waters et al. 2006). Although
most of these models suffer from a warm bias in tropical
tropopause temperature, the CMIP5 models do not, on
average, show a moist bias in the stratospheric water
vapor, and if anything appear slightly dry in the stratosphere. It is likely, then, that there are many processes
influencing stratospheric water vapor in the CMIP5 climate models—some by altering upper-tropospheric
water vapor concentrations directly, which will then be
advected by the model into the lower stratosphere as a
result of the approximations inherent in the model
vertical advection scheme (regardless of the cold-point
temperature; see section 3), and some by altering the
cold-point temperature and therefore indirectly influencing stratospheric water vapor concentrations.
The dipolar nature of the temperature bias in the
upper troposphere and lower stratosphere suggests that
this bias is related to the model incorrectly simulating
the height of the tropopause. Figure 5a shows that the
height of the cold point is, accurate to within the vertical
resolution of the output data, at 100 hPa. Figure 5b
shows the ‘‘thermal tropopause’’ defined, conventionally, as the height at which the lapse rate, 2dT/dz (where
T is temperature and z is geometric height), drops below
the value 2 K km21 (WMO 1957). Comparing Fig. 5b
with Fig. 3 demonstrates that the bias in the height of the
tropopause is indeed strongly related to the magnitude
of the dipole in the temperature bias in the upper troposphere and lower stratosphere. Therefore, any process reducing the magnitude of a model temperature
bias in the TTL region also has the potential to reduce
any bias in the modeled tropopause height.
6520
JOURNAL OF CLIMATE
VOLUME 28
FIG. 4. Bias with respect to MERRA in modeled annual-mean stratospheric water vapor q area averaged from 108S to
108N for the HadGEM3 model versions GA4.0 and GA6.0 (see Table 1), plotted (a) in absolute terms (ppmv) and (b) as
percentage above the reanalysis concentrations. (c) The absolute bias (ppmv) with respect to MERRA for all CMIP5 models
for which data were available in the central archive (colors and shading as in Fig. 2). The 1990–99 period is used.
For the remainder of the paper, we will focus on the
tropical temperature at 100 hPa (area averaged from
108S to 108N) as a measure of the cold-point temperature. We also use specific humidity q at 70 hPa (area
averaged from 108S to 108N) as a measure of water vapor
concentrations entering the stratosphere in the tropics.
An altitude of 70 hPa is within the stratosphere, avoiding
the complications of vertical mixing across the cold
point that would arise from considering q at 100 hPa. It is
also below the height of the majority of water vapor
production by stratospheric oxidation of methane, and
no other local processes in the tropical stratosphere are
believed to influence stratospheric water vapor concentrations. Of course, the horizontal mixing of extratropical air into the tropics can still influence tropical
water vapor concentrations at 70 hPa.
c. Sensitivity experiments
Sensitivity experiments, modifying numerical, microphysical, and radiative processes, were carried out from a
common baseline (GA6.0). The experiments having a
significant impact on cold-point temperature and stratospheric water vapor are summarized in Table 2, and are
referred to below using the names given in Table 2.
While some of these sensitivity experiments represent
improvements to the model physics and some are simply a
modification of model parameters, they all represent a
change in the model representation of a process that has
an impact on tropical tropopause temperature and/or
stratospheric water vapor. Sensitivity experiments were
also carried out in which the magnitude of nonorographic
gravity wave drag was altered, with the aim of modifying
the strength of the stratospheric Brewer–Dobson circulation (Butchart 2014), known to impact the cold-point
temperature (Gettelman et al. 2010; Dessler et al. 2014).
However, in HadGEM3 this was found to have very little
impact on the tropopause temperature or water vapor
biases, and so, along with other sensitivity experiments
having insignificant impact (detailed in section 5), this is
not considered further.
As previously discussed, one way to influence lowerstratospheric water vapor in the model is to change the
cold-point temperature. In the real atmosphere, the water
vapor in a fluid parcel in the lower stratosphere will be
determined by the lowest saturation vapor mixing ratio
encountered on its route there (Gettelman et al. 2010).
15 AUGUST 2015
HARDIMAN ET AL.
6521
FIG. 5. (a) Annual-mean tropical temperature area averaged from 108S to 108N in the different configurations of
the MetUM (see Table 1) and the reanalyses ERA-I and MERRA. For the resolution of output data available, the
cold-point temperature is found to be located at 100 hPa. (b) Thermal lapse rate, calculated as 2dT/dz (K km21), for
the models, ERA-I, and MERRA. The thermal tropopause is defined as the height at which the lapse rate drops to
values below 2 K km21 (shown by thin vertical black line).
This behavior is incorporated into HadGEM3 using the
Goff–Gratch formulation for the saturation water vapor
pressure (Goff 1965). The modeled water vapor concentrations in the lower stratosphere will depend not only
upon this formulation, but also on the fact that not all air is
fully dehydrated as it passes through the TTL and, furthermore, on the approximations inherent in the model
advection and parameterization schemes (see sections 3
and 4 below). Furthermore, the saturation vapor mixing
ratio is a nonlinear function of temperature, and so the
zonal-mean monthly mean temperature at 100 hPa, area
averaged from 108S to 108N, being used in this study as a
measure of the cold-point temperature, is clearly just a
proxy for the temperature at the times and locations where
the air in the lower stratosphere will have been saturated
(Fueglistaler and Haynes 2005).1 Therefore, the following
approach is taken to attempt to determine when a change
in the modeled lower-stratospheric water vapor concentration is almost entirely due to a change in the cold-point
temperature. Figure 6 shows monthly mean zonal-mean
values of T(108S–108N, 100 hPa) and q(108S–108N, 100 hPa),
plotted against each other for every month of every
simulation considered in this paper.2 These points are
1
However, the model’s representation of variability in temperature at 100 hPa is found to be realistic. The standard deviation of
the simulated daily temperatures at 100 hPa differs from ERA-I by
only around 60.4 K (not shown).
2
This empirical relationship needs defining using q(108S–108N,
100 hPa), which correlates with T(108S–108N, 100 hPa) with a
correlation coefficient of 0.9, while q(108S–108N, 70 hPa) only
correlates with T(108S–108N, 100 hPa) with a correlation coefficient of 0.3. It remains the case, as discussed in the previous
section, that q(108S–108N, 70 hPa) is a better measure of stratospheric water vapor concentrations than q(108S–108N, 100 hPa).
found to fit very closely to a straight line (Fig. 6), the
gradient of which is 0.39 ppmv K21 in the model simulations (as compared to 0.41 ppmv K21 in ERA-I). In what
follows, this gradient is referred to as the empirically
derived Clausius–Clapeyron relation (for HadGEM3).
Physical processes that lead to changes in the cold-point
temperature and lower-stratospheric water vapor following this empirical relationship are considered to have
changed the lower-stratospheric water vapor concentrations solely by changing the cold-point temperature.
Those that do not follow this relationship are considered
to have directly impacted the stratospheric water vapor
concentrations.
Figure 7 shows the effect of the processes detailed in
Table 2 on both the cold-point tropical tropopause
temperature (T at 100 hPa, area averaged from 108S to
108N) and the lower-stratospheric water vapor concentration (q at 70 hPa, area averaged from 108S to 108N),
averaged over the period 1999–2008. Figure 7 shows the
biases in temperature and water vapor relative to both
MERRA and ERA-I. The differences between the reanalyses give an indication of the uncertainty in the true
value of both quantities, but both reanalyses are in good
agreement with observations [see Fig. 3, which shows a
comparison to the RICH-adjusted temperature dataset,
and the top left-hand panel of Fig. 4 in Hegglin et al.
(2013), which shows the multimodel mean value for zonalmean water vapor over the period 1998–2008, inferred from
several satellite datasets]. A positive bias exists relative to
both reanalyses in both temperature and water vapor. It can
be seen that the u vertical advection–interpolation, u vertical
advection–conservation, radiative heating, ice optics, and
ozone radiative feedback processes, which influence temperature alone (either through advection or via the radiation
scheme) follow the empirically derived Clausius–Clapeyron
6522
JOURNAL OF CLIMATE
VOLUME 28
TABLE 2. Sensitivity experiments.
Process name
Modifications to model
u vertical advection–interpolation
Interpolation in vertical advection scheme for potential temperature changed from cubic
Lagrange to cubic Hermite.
Priestley conservation algorithm is applied to interpolated potential temperature in
vertical advection scheme.
Interpolation in vertical advection scheme for moisture changed from quintic Lagrange
to cubic Hermite.
An improved representation of gaseous absorption.
An improved representation of the optical properties of atmospheric ice crystals.
An improved representation of the ice particle size distribution and fall speeds of ice
crystals.
Improvements to the vertical transport of heat and moisture by the convective
parameterization.
Change the rate at which unresolved motions act to spread cirrus cloud in the horizontal.
Interactively simulated ozone passed to model radiation scheme, instead of prescribed
ozone climatology.
u vertical advection–conservation
q vertical advection–interpolation
Radiative heating
Ice optics
Ice microphysics
Convection
Cirrus spreading rate
Ozone radiative feedback
relation very closely, as expected. However, the other
processes (q vertical advection–interpolation, ice microphysics, convection, and cirrus spreading rate) directly
influence the water vapor concentrations in the upper
troposphere, and do not follow this relation at all. The
following sections describe the processes represented in
Fig. 7 in more detail, giving mechanisms for their influence on tropical tropopause temperature and stratospheric water vapor, and explaining how water vapor
concentrations in the upper troposphere can have just as
much influence on lower-stratospheric water vapor in the
model as can the cold-point temperature.
it is desirable to use a cubic scheme also for the vertical
advection. However, the SL advection algorithm traditionally chosen, using a four-point cubic Lagrange interpolation stencil (Staniforth and C^
oté 1991), only
provides first-order temporal accuracy in the tropopause
region because the interpolation stencil shifts with the
direction of the vertical wind (see Fig. 8). For upward
advection at the tropopause, the interpolation stencil has
more points in the troposphere than the stratosphere
whereas for downward advection the interpolation stencil
contains more stratospheric points than tropospheric
3. Numerical accuracy of vertical advection in the
tropical tropopause layer
The tropopause is characterized by a rapid change in the
vertical gradient of potential temperature. In the tropics,
the vertical wind in the vicinity of this persistent thermal
structure has a very small mean ascent [less than 1 mm s21
on the annual mean; see Fig. 1a of Hardiman et al. (2014)].
Superposed on this mean ascent are wavelike motions with
vertical velocities much larger than the mean (Orbe et al.
2012), but whose time scale is short enough for diabatic
effects and mixing to be negligible, making them essentially physically reversible. Model performance in this region is consequently sensitive to how well the vertical
advection scheme performs under such flow conditions.
In a model using a semi-Lagrangian (SL) advection
scheme, interpolation of model fields to trajectory departure
points is required. The model evolution is therefore sensitive
to the polynomial used for this interpolation, and to the
set of points used to fit this polynomial (known as the
interpolation ‘‘stencil’’).
In HadGEM3, a SL scheme with bicubic Lagrange
interpolation is used for horizontal advection. Therefore,
FIG. 6. Sensitivity of q(108S–108N, 100 hPa) to changes in T(108S–
108N, 100 hPa), as determined from values of monthly mean zonalmean q and T for all months from 1999–2008 for all model integrations
included in this study (black plus signs) and ERA-I (red plus signs).
There are 10 model integrations included (one control and one for
each of the nine processes listed in Table 2) and therefore 10 3 10 3
12 5 1200 model values shown, and 10 3 12 5 120 values shown for
ERA-I. The blue line shows the linear least squares fit to the model
values, and has a gradient of 0.39 ppmv K21 (the equivalent gradient
for ERA-I is 0.41 ppmv K21, not shown).
15 AUGUST 2015
HARDIMAN ET AL.
FIG. 7. The effects of different processes on the model biases in
annual-mean tropical temperature T at 100 hPa area averaged from
108S to 108N, and water vapor q at 70 hPa area averaged from 108S to
108N, with respect to MERRA and ERA-I. The direction of the
arrows shows the changes due to these processes as described in
sections 3 and 4. Ten-year climatologies for the period 1999–2008 are
used. Apart from u vertical advection–interpolation (marked by
a light green square) all other changes (marked by asterisks) are
relative to GA6.0 (marked by a black triangle). The thin black line
shows the derived sensitivity of q to T in model simulations (gradient
of blue line in Fig. 6). All the changes to T and q due to these processes are statistically significant at the 95% level (using a Student’s
t test), with the exception of the changes to T because of q vertical
advection–interpolation, convection, and cirrus spreading rate. The
value of T(108S–108N, 100 hPa) in ERA-I is 192.4 K and in MERRA
is 193.1 K. The value of q(108S–108N, 70 hPa) in ERA-I is 3.49 ppmv
and in MERRA is 3.74 ppmv. The ‘‘observed’’ value of q(108S–108N,
70 hPa), inferred from the SWOOSH dataset (NOAA/Earth
Systems Research Laboratory 2014), is 3.76 ppmv.
ones (Fig. 8). As a result the upward advection is predominantly affected by the tropospheric gradient of potential temperature, while the downward advection is
dominated by the stratospheric gradient. The stratospheric
potential temperature gradient is systematically larger than
the tropospheric gradient. Hence there is a lack of cancellation between the upward (cooling) and downward
(warming) phases of vertical advection, breaking the reversibility and resulting in a net warming at the tropopause.
Cubic Hermite interpolation (Williamson 1990) removes this dependence on the direction of the vertical
wind by constructing a cubic polynomial from two values
of the function and its derivative collocated at two distinct
points. When used for SL advection, numerical approximations are required to provide the derivatives. If these
derivative approximations are constructed with a symmetric (centered) stencil, then the SL scheme will assign a
single value for the derivative at each grid point, regardless of the wind direction. Consequently, the SL scheme
with cubic Hermite interpolation has second-order
6523
FIG. 8. Schematic of cubic Lagrange and cubic Hermite interpolation for the vertical advection of potential temperature. For
cubic Lagrange interpolation, upward advection is predominantly
affected by the tropospheric gradient of potential temperature, and
downward advection is predominantly affected by the stratospheric gradient of potential temperature. As such, the upward
cooling and downward warming effects do not balance, as they
should for an oscillation of small amplitude, and a spurious heating
is introduced in the region of the tropical tropopause. Since cubic
Hermite uses a continuous reconstruction of the vertical derivative
for both upward and downward advection, it eliminates this spurious heating.
temporal accuracy for small oscillations at the tropopause. Using cubic Hermite for the vertical advection of
potential temperature thus reduces the spurious heating
of the tropopause associated with the cubic Lagrange
scheme, significantly reducing the warm bias in the
tropical tropopause temperature (see Fig. 9). The temperature bias is reduced by 2.25 K (compared to using
cubic Lagrange interpolation) and in line with the empirically derived Clausius–Clapeyron relation the water
vapor bias is reduced by 0.90 ppmv (light green line in
Fig. 7). These changes are statistically significant at the
95% level (as are all changes in Fig. 7 with the exception
of three, as noted in the figure caption). For this reason,
cubic Hermite interpolation for potential temperature
was included in GA6.0 (i.e., this is the only change
shown in Fig. 7 that does not start from GA6.0).
The vertical advection scheme applied to moisture will
also influence the model biases (Stenke et al. 2008). Replacing quintic Lagrange with cubic Hermite interpolation for the vertical advection of moisture results in a
significant reduction in the stratospheric water vapor bias
of 0.81 ppmv (light blue line in Fig. 7). A further numerical
consideration, affecting the choice of vertical advection
scheme for water vapor, is the way in which this scheme is
coupled to the cloud microphysical processes discussed in
section 4b. These processes are treated as source terms
evaluated at the departure points of the SL advection
scheme. In the tropopause layer, where freeze-drying is
taking place, the amount of water vapor available for
ascent is largely determined by a combination of ice
6524
JOURNAL OF CLIMATE
VOLUME 28
FIG. 9. Zonal annual-mean temperature bias with respect to ERA-I in (a) GA6.0 but using cubic Lagrange interpolation for the vertical
advection of potential temperature, rather than cubic Hermite, (b) GA6.0, and (c) GA6.0 using Priestley conservation scheme for the
vertical advection of potential temperature. The difference between (a) and (b) shows the effect of the interpolation scheme, and the
difference between (b) and (c) shows the effect of including conservation, in the vertical advection of potential temperature. In all panels,
the zero line is shown by a thick solid contour, positive biases by thin solid contours, and negative biases by thin dashed contours. The
1999–2008 period is used.
microphysics and the Clausius–Clapeyron equation.
Advection schemes that interpolate by fitting a polynomial over several vertical model levels will inevitably
incorporate information about the water vapor saturation
vapor mixing ratios at model levels below and above the
cold point, as well as at the cold point itself. The resulting
saturation vapor mixing ratio determining water vapor
concentrations on entry to the stratosphere is thus
influenced by water vapor concentrations in the upper
troposphere and not just at the cold point (i.e., there
is a spatial smoothing of the saturation vapor mixing
ratio), potentially weakening the tight coupling between stratospheric water vapor concentrations and coldpoint temperature derived in Fig. 6. Analysis (C. Smith
et al. 2015, unpublished manuscript) shows that the
cubic Hermite scheme has lower spatial smoothing in
this respect than does the quintic (or cubic) Lagrange
scheme, leading to more realistic stratospheric water
vapor concentrations.
Further support for the benefit of the cubic Hermite
interpolation method is given in Fig. 10, which shows the
time evolution of the vertical profile of water vapor area
averaged from 108S to 108N, demonstrating the so-called
tape-recorder (Mote et al. 1996) effect. The seasonal
cycle in the cold-point temperature imposes a seasonal
cycle in the stratospheric water vapor concentrations,
which is advected upward through the tropical stratosphere, allowing the mean ascent rate there to be diagnosed from the slope of the tape-recorder plots.
Comparing to observed water vapor inferred from the
Stratospheric Water and Ozone Satellite Homogenized
dataset (SWOOSH; NOAA/Earth Systems Research
Laboratory 2014), it is found that using cubic Hermite
vertical interpolation for moisture as well as for potential
15 AUGUST 2015
HARDIMAN ET AL.
6525
FIG. 10. Tropical ‘‘tape recorder’’ of monthly mean specific humidity q (ppmv) area averaged from 108S to 108N in (a) SWOOSH,
(b) GA6.0 (cubic Hermite vertical interpolation for potential temperature and quintic Lagrange vertical interpolation for q), and
(c) GA6.0 with cubic Hermite applied to q (as well as potential temperature). For the period 1999–2005, the SWOOSH dataset is a merged
product of HALOE data (Russell et al. 1993) and Aura MLS (Waters et al. 2006), with HALOE data being bias corrected to match the
Aura MLS mean as a function of latitude and pressure. From 2005 onward, the SWOOSH product is purely Aura MLS data.
temperature gives not only a better mean water vapor
concentration throughout the stratosphere, but also a more
coherent tape-recorder signal in the midstratosphere.
Improvements have therefore been made by using the
same interpolation scheme for the advection of moisture
and potential temperature. However, for reasons that
have evolved as the model has developed, differences
between how moisture and potential temperature are
advected remain. Specifically, a mass-conserving algorithm (Priestley 1993) is applied to the interpolated
moisture mixing ratios to ensure that the corrected interpolated values preserve the global integral of moisture. This is because interpolation of the mixing ratios
does not, except in very special circumstances, preserve
mass integrals of the advected quantity. However, in
GA6.0 this procedure is not applied to potential temperature despite the fact that analytically the product of
dry mass and potential temperature is a conserved
quantity (for adiabatic flows). In the spirit, then, of
striving to transport moisture and potential temperature
in a consistent way, an experiment (labeled ‘‘u vertical
advection–conservation’’ in Table 2) was conducted in
which the mass-conserving algorithm (Priestley 1993) is
also applied to potential temperature. The impact of this
change is indicated by the dark green line in Fig. 7. There
is a clear reduction of both the temperature and humidity
biases (of 0.79 K and 0.36 ppmv respectively) in the region of the tropical tropopause. It seems likely that the
errors in conservation of potential temperature are
preferentially committed where the gradient of potential
temperature changes most rapidly (i.e., there is a lack of
smoothness in the vertical potential temperature profile).
The tropical tropopause is such a location (see Fig. 9).
4. Physical processes in the tropical tropopause layer
The main physical mechanism by which the temperature of the TTL is determined is radiative heating by
6526
JOURNAL OF CLIMATE
ozone, water vapor, and carbon dioxide. This radiative
heating occurs from both shortwave radiation (direct
from above or reflected from below) and longwave radiation emitted from the troposphere and surface below.
Therefore, the temperature can be modified either by
changing the radiation parameterization directly, changing the physical properties of the troposphere that affect
upwelling longwave radiation (mainly clouds), or changing the atmospheric concentrations of ozone, water vapor,
and carbon dioxide.
The water vapor concentration of the lower stratosphere is determined by the vertical transport of water
vapor from the tropical troposphere. Changes to the
cold-point temperature (as discussed in section 2c) or to
the water vapor concentration in the upper troposphere
(as discussed in the previous section) can modify water
vapor concentrations in the lower stratosphere. Water
vapor concentrations in the upper troposphere can be
modified by changes to the parameterized vertical
transport of water through the troposphere (i.e., by
convection and precipitation), as discussed below.
a. Radiation changes
Developments beyond GA6.0 include two changes to
the radiation scheme that improve the physical basis of
the scheme with respect to recent observational and
theoretical results.
1) RADIATIVE HEATING
First, the treatment of gaseous absorption has been
modified based on the high-resolution transmission
molecular absorption database (HITRAN 2012; see
Rothman et al. 2013). This makes use of new correlatedk techniques (J. Manners 2015, unpublished manuscript)
to improve the scaling of absorption with pressure and
temperature leading to an improved representation of
stratospheric heating or cooling (resulting from water
vapor, ozone, and carbon dioxide). The consequence of
this is that more radiation (both longwave and shortwave) is absorbed by stratospheric gases, leading to a
direct heating of the tropical tropopause and a 0.74-K temperature increase. This temperature increase directly leads
to a humidity increase, in line with Clausius–Clapeyron,
which is advected upward into the stratosphere, resulting in a 0.32 ppmv increase in humidity (yellow line in
Fig. 7).
2) ICE OPTICS
Second, a new treatment of the cirrus bulk optical
properties based on Baran et al. (2014) is included. An
ensemble of ice crystal shapes with known singlescattering properties is used by the microphysics parameterization scheme. These are distributed over the
VOLUME 28
ice particle size distribution to calculate the bulk optical
properties in each grid box. This removes the strong
temperature dependence that characterized the previous scheme (Edwards et al. 2007), which was based on
directly parameterizing the ice-crystal effective diameter as a function of temperature.
For cold tropical cirrus clouds (between temperatures
of 210 and 235 K) the inclusion of this new scheme leads
to a reduction in longwave absorption. At near-infrared
wavelengths the single scattering albedo of ice has also
been significantly reduced, leading to increased shortwave absorption (by around 30%) and reduced reflection from these cold high clouds. The reduction in
longwave extinction within the cirrus clouds leads to an
increase in upwelling longwave radiation originating
from the warmer layers below the cloud. This in turn
leads to a relative increase in the heating at the level of
the tropical tropopause, as the increased radiation is
absorbed by stratospheric gases. This effect dominates
over the reduction in upwelling near-infrared radiation
reflected from the cloud top. The tropical tropopause
temperature is increased by 0.86 K, with a corresponding
increase of 0.38 ppmv in humidity, again dominated by
the Clausius–Clapeyron relation (pink line in Fig. 7).
b. Ice cloud changes
The distribution of high ice cloud influences TTL
conditions in two ways. It indirectly affects the temperature structure of the layer because of the interaction of
ice crystals with solar and terrestrial radiation (Dinh and
Fueglistaler 2014). The mechanism for this is identical
with that described above when changing the optical
properties of the ice cloud, except that we are now
changing the concentration, location and extent of the ice
cloud. However, it has a direct effect on the humidity
structure of the layer because ice cloud acts as a sink of
water vapor. Therefore ice cloud changes can potentially
affect the stratospheric water vapor in ways different
from those predicted by Clausius–Clapeyron arguments.
Three changes are discussed which affect the concentration, location, and extent of the ice cloud.
1) ICE MICROPHYSICS
The main microphysical processes controlling high
cloud are particle sedimentation and depositional
growth. Modeling these processes requires assumptions
about the ice PSD, fall speeds, and mass–diameter relationship. In GA6.0 these properties are either based
on antiquated in situ datasets, known to suffer from
systematic errors, or have been arbitrarily tuned.
We have investigated making these three properties
physically more realistic by taking them from more
modern data sources [for more details, see Furtado et al.
15 AUGUST 2015
HARDIMAN ET AL.
(2015)]. The main benefit, in terms of physical realism,
comes from using measurements that have been corrected, as far as possible, for contamination by artifacts
caused by ice crystal fragmentation on the housings of
airborne instrumentation (‘‘shattering’’; Korolev and
Isaac 2005, Field et al. 2006). Sensitivity experiments
reported in Furtado et al. (2015) showed that the effect
of these combined changes is to increase the amount of
high cloud, further humidify the TTL, warm the troposphere, and cool the lower stratosphere. These effects
can be qualitatively understood as follows.
The parameterization changes reduce the ice crystal fall
speeds compared to GA6.0. This significantly decreases
the mean sedimentation flux of ice, which causes an increase in the amount of high cloud. This effect can be
viewed as a consequence of total water conservation:
convection and resolved dynamics transport water (vapor
and condensate) vertically upward and, in steady-state
conditions, this is balanced by downward sedimentation of
ice crystals. If a parameterization change decreases the ice
sedimentation flux while the upward water flux remains
the same, then the ice water content will increase until
sedimentation rebalances the upward water transport.
An effect of the PSD change is to increase the characteristic time scale for depositional growth of ice. At
low temperatures the GA6.0 PSD apportions a large
fraction of any given population of ice crystals to sizes
less than 100 mm. These small crystals provide a very
efficient sink of water vapor. Relatively fewer small
particles with the PSD change implies that depositional
growth occurs less rapidly, allowing the amount of water
vapor in the TTL to build up to higher levels. The
combination of PSD and fall speed changes results in a
0.22 ppmv increase in stratospheric water vapor, shown
by the purple line in Fig. 7.
The effect of increased ice cloud on the temperature
structure of the TTL and troposphere is determined by
cloud-radiative effects, as discussed previously (see
also Seiki et al. 2015). Figure 11 shows the zonal-mean
tropical temperature change due to the microphysics
changes. The climate response has a dipolar structure
in the vertical: tropospheric warming, because of intensification of the infrared–greenhouse gas forcing, is
accompanied by a cooling of the cold point (of 0.44 K)
that extends well into the lower stratosphere, because
of reduced infrared transmission to the TTL region and
gaseous absorption. Comparison with Fig. 3 shows that
these changes improve the model climatology, both in
the troposphere and in the stratosphere.
2) CONVECTION
The convective parameterization is the main mechanism in the troposphere by which heat and moisture are
6527
FIG. 11. Zonal annual-mean structure of the temperature differences (K) in the tropics induced by the changes to the ice microphysics parameterizations for the period 1999–2008. Contours
show temperature difference from GA6.0.
transported vertically in the tropics, and typically it
takes warm, moist air from near the surface and distributes it throughout the troposphere. In GA6.0, the
convective parameterization had a tendency not to
convect high enough into the upper troposphere. This
was found to be partly due to numerical approximations made in calculations of air parcel properties
during the parcel ascent.3 Therefore, this aspect of the
scheme was improved with the consequence that the
scheme carries more buoyancy and can penetrate further into the TTL.
This has two consequences for TTL temperatures.
First, the direct detrainment of cirrus cloud from the
convective plume occurs at a higher altitude, bringing the
modeled cloud height closer to observations (see Fig. 12).
Therefore, the cloud tops are colder, and thus emit less
longwave radiation into the TTL region, directly reducing
the radiative heating. Second, the increased moisture
carried by the convective plume means that the detrained
cloud contains a greater mass concentration of ice. In
exactly the same manner as reducing the sedimentation
rate of ice crystals reduced the TTL temperature, increasing the source of ice has the same effect.
Early versions of the improved convective parameterization had a significantly detrimental effect on the
stratospheric water vapor, because of the increased
moisture carried upward by the convective plume. Once
deposited in the upper troposphere, this increased
3
These approximations were reasonable in older versions of the
model (HadGEM2 and its predecessors; see Table 1) because of
the lower vertical resolution but are no longer accurate at the enhanced vertical resolution of HadGEM3.
6528
JOURNAL OF CLIMATE
VOLUME 28
by 0.15 K (orange line in Fig. 7). Furthermore, these
modifications have the additional benefit of significantly
improving the tropical precipitation (distribution and
amount) in all seasons (not shown).
3) CIRRUS SPREADING RATE
FIG. 12. The 20-yr mean vertical profile of cloud frequency over
the warm pool (108N–108S, 1108E–1808) for GA6.0 and GA6.0 with
modified convection scheme using the Cloud–Aerosol Lidar and
Infrared Pathfinder Satellite Observation (CALIPSO) simulator
(Chepfer et al. 2008) from the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package
(COSP; Bodas-Salcedo et al. 2011). The observed profile from
CALIPSO is shown in black.
moisture will act, via the interpolation routines in the
vertical advection scheme, to increase the water vapor
concentration in the lower stratosphere. In fact, this
impact, detrimental to GA6.0 and seen because of improving the height of convection, was a result of removing one of two canceling errors. The MetUM uses an
‘‘adaptive detrainment’’ parameterization as described
in Derbyshire et al. (2011). As a rising convective plume
detrains material to the surrounding environment, it
loses buoyancy. The adaptive detrainment parameterization dictates that much of the material detrained is
selectively that which is already neutrally buoyant with
respect to the surroundings, such that the loss of buoyancy by the plume will be less than otherwise expected
and the plume will rise higher. Because of its tendency
not to convect high enough, the adaptive detrainment
parameterization in GA6.0 was tuned so that very large
fractions of neutrally buoyant material were detrained—
notably, greater fractions than were considered realistic
by Derbyshire et al. (2011). With the improvements already discussed naturally making the plume more buoyant, it is no longer necessary to detrain such high fractions
of neutrally buoyant material, allowing the adaptive detrainment parameterization to be used within the bounds
recommended by Derbyshire et al. (2011). The net effect
of improving the height of convection and the modification of the adaptive detrainment parameterization is to
increase the lower-stratospheric water vapor concentration by 0.30 ppmv and reduce the cold-point temperature
In GA6.0, any ice cloud cover is assumed to gradually
spread out, driven by unresolved horizontal mixing
within a grid box. In the absence of additional sources,
this increases the gridbox mean cloud cover while keeping the ice water content constant. The precise value of
this spreading rate is highly uncertain and unconstrained.
Therefore, as a sensitivity test, we reduced its value to
effectively zero to stop cirrus clouds from increasing in
area over time, decreasing the high cloud fraction without
significantly altering the gridbox mean ice water content.
This was done purely to investigate what effect the areal
extent of cirrus cloud has on the TTL temperature and
stratospheric humidity.
Reducing the cloud fraction increases the temperature
by 0.30 K (gray line in Fig. 7). This is a similar mechanism
to the ‘‘ice optics’’ change. The increased area of clear sky
allows more upwelling longwave radiation to interact
with stratospheric gases, heating the TTL. Again, this
effect dominates over the corresponding reduction in
shortwave reflection from the cloud top.
However, the reduced cloud fraction decreases the
amount of water vapor in the TTL by 0.18 ppmv. This is
because even though changing the spreading rate does
not directly influence the ice water content, compacting
the same ice water content into a smaller cloud fraction
leads to a feedback. The time scale for collision and coalescence of ice crystals is reduced (because they are
packed closer together), and therefore the growth and
sedimentation of ice crystals is enhanced. This increases
the precipitation sink of water from the upper troposphere, which in the absence of any changes to the source
(convection) reduces water vapor concentrations. It
therefore has the opposite effect to the ‘‘ice microphysics’’ changes, which acted to reduce this sink.
c. Chemistry
To properly model the climate system, models need to
include interactions with chemical species (Nowack
et al. 2015) and biological components (Ciais et al. 2013)
in addition to the processes already discussed. A model
that includes these interactions is described as an Earth
system model (ESM). Here, interactive chemistry from
the United Kingdom Chemistry and Aerosol (UKCA)
stratosphere–troposphere chemistry scheme is added to
GA6.0. This scheme combines the stratospheric chemistry scheme of Morgenstern et al. (2009) with the
‘‘TropIsop’’ tropospheric chemistry scheme detailed in
15 AUGUST 2015
HARDIMAN ET AL.
6529
FIG. 13. Impact of interactive ozone on temperature bias: (a) total impact, (b) impact due to ozone climatology, and (c) impact due to
ozone coupling.
O’Connor et al. (2014), and uses the ‘‘Fast-JX’’ photolysis scheme of Telford et al. (2013).
Including interactive chemistry potentially allows
significant radiative feedbacks on the model temperature from methane (CH4), nitrous oxide (N2O), and
ozone (O3), all of which act as greenhouse gases.
Methane and nitrous oxide are found to have some
impact on the tropical tropopause temperature, via the
model radiation scheme. Methane reduces the cold-point
temperature by 0.32 K and the lower-stratospheric humidity by 0.13 ppmv, and nitrous oxide reduces the coldpoint temperature by 0.46 K and the lower-stratospheric
humidity by 0.17 ppmv (not shown). In the interactive
case, the radiation scheme sees lower methane and nitrous oxide concentrations than in the prescribed case,
leading to this cooling and drying. Ozone has the opposite effect, and is found to have a more significant
impact, raising the cold-point temperature by around
1.13 K and the lower-stratospheric humidity by 0.55
ppmv in line with the Clausius–Clapeyron relation (dark
blue line in Fig. 7).
Three model integrations were carried out as experiments in which the radiation scheme was passed three
different ozone fields, namely 1) prescribed climatological
‘‘observed’’ ozone from the Atmospheric Chemistry and
Climate(AC&C)/Stratospheric Processes and their Role
in Climate (SPARC) ozone database (Cionni et al. 2011),
2) prescribed climatological ozone as simulated by UKCA,
and 3) UKCA interactive ozone. Using these integrations
it is possible to separate the total effect of radiative feedback from UKCA ozone on the modeled temperature
(experiment 3 minus experiment 1; Fig. 13a) into that
arising from the ozone climatologies being different (experiment 2 minus experiment 1; Fig. 13b) and that arising
from ozone coupling to the radiation scheme per se (experiment 3 minus experiment 2; Fig. 13c). It is found that
almost the entire impact of the interactive ozone comes
from the fact that the UKCA ozone climatology is different from that of the AC&C/SPARC ozone database.
Currently the most realistic ozone database available
is the Tier1.4 vertically resolved ozone data built on the
Binary Data Base of Profiles (BDBP; Bodeker Scientific
6530
JOURNAL OF CLIMATE
VOLUME 28
FIG. 14. UKCA ozone concentrations vs the BDBP vertically resolved ozone climatology: (a) absolute difference (ppbv) and
(b) relative difference (%). On average, over the whole altitude range shown, the UKCA ozone concentrations in the region 108S–108N
are 16% above those observed.
2008; Hassler et al. 2008, 2009). This database includes
the Stratospheric Aerosol and Gas Experiment satellite
(SAGE I and II) measurements used to construct the
AC&C/SPARC ozone database, along with additional
measurements from the Polar Ozone and Aerosol Measurement (POAM II and III) satellite instruments (see
Hassler et al. 2008). Comparing the UKCA modeled ozone
concentrations with those from the BDBP confirms that,
on average in the tropics (108S–108N), the UKCA modeled
ozone concentrations are around 16% higher than those
observed. Figure 14 shows that, while in absolute terms, the
difference between the modeled and these observed
ozone concentrations is greatest around 20–30 km, in relative terms the difference is greatest in the tropical tropopause region (up to 80%). It is this that leads to such a large
impact on the modeled tropical tropopause temperature.
The question of how to improve the simulated interactive ozone concentrations is a complex one. Using
an identical chemistry scheme, with identical emissions,
the GA4.0 model configuration produces a maximum
ozone bias of around 40% in the tropical tropopause
region (not shown), as compared to 80% in GA6.0
(Fig. 14b). This is possibly due to the different dynamical
cores used in GA4.0 and GA6.0 (see references in Table 1)
causing the vertical transport of ozone into and out of
this region to be different. Thus, the interaction between
the physical and composition components of an ESM is
complex, and crucial to the performance of the ESM.
5. Discussion and conclusions
This paper has documented the effects of the dynamical, radiative, and microphysical processes having
the largest impact on the tropical tropopause temperature, and the value of water vapor concentrations on
entry to the stratosphere, in the latest version of the Met
Office Unified Model (MetUM).
The numerical accuracy of vertical advection across
the tropopause, determined in the MetUM by the interpolation and conservation schemes used within the
advection routine, is found to be particularly important.
Within the interpolation scheme, it is found necessary to
ensure that small-amplitude wavelike motions across
the changes in the vertical gradients of potential temperature and humidity at the tropopause are treated so
as to be essentially reversible, and that any spatial
smoothing of water vapor concentrations in the upper
troposphere inherent in the vertical interpolation is kept
to a minimum. Applying a conservation scheme, such
that mass integrals of potential temperature and water
vapor are conserved during vertical advection, is also
found to be essential. Improving the vertical advection
in this way, the modeled tropical tropopause temperature and lower-stratospheric water vapor concentrations
are brought significantly closer to their observed values.
Modifications to all processes discussed in this paper,
with the exception of those to the model advection
schemes, were found to have a detrimental impact on
either the cold-point temperature bias or the lowerstratospheric water vapor bias. For some of the processes considered, such as the microphysical properties
of ice particles (e.g., their particle size distribution and
mass–diameter relationship), there exist observations
under some atmospheric conditions (Field et al. 2007;
Cotton et al. 2013) that can be used to constrain the
model parameterization. It is reasonable, in the absence
15 AUGUST 2015
HARDIMAN ET AL.
of other observations for those processes, to extrapolate
these observations to the tropical tropopause region.
Model improvements to the representation of radiative
absorption, ice particle size distributions, and ice fall
speeds (in the sense of moving closer to these extrapolated observations) will impact both the temperature
and the water vapor concentrations in the tropical tropopause region. The fact that these improvements were
found to have a detrimental impact on the modeled
cold-point temperature and lower-stratospheric water
vapor concentrations (relative to GA6.0) is suggestive of
the continuing presence of previously compensating
errors, or of the fact that there are further improvements
still required to these parameterizations.
Further sensitivity was found to convective and cirrus
cloud processes. There are no observational constraints for
the adaptive detrainment rate in the convection scheme
(Derbyshire et al. 2011) or for the cirrus spreading rate in
the cloud scheme. Therefore, while cloud heights are
reasonably well observed, there remains significant flexibility for modification of parameters within the convection
and cloud schemes to improve the simulation of wellconstrained model fields, such as temperature. These
schemes contribute significantly to the uncertainty in
upper-tropospheric water vapor concentrations.
The importance of the vertical distribution of ice and
water vapor in the TTL, and in particular a model’s
representation of deep convection, cloud radiative, and
ice-phase microphysical processes on its simulated TTL
temperatures, is discussed also in Evan et al. (2013). The
processes influencing tropical tropopause temperature and
lower-stratospheric water vapor discussed in the current
paper fall into three categories: dynamical processes
(model advection), radiative processes (sensitive to water
vapor and ice, cloud cover, and greenhouse gases), and
microphysical processes that act as upper-tropospheric
water vapor sources (tropical convection) and sinks (ice
particles). How efficient the ice is as a sink of water vapor is
dependent on the ice particle size distribution and fall
speed. The ice crystal growth rate and sedimentation flux is
itself sensitive to cirrus cloud fraction.
The impacts of all these processes on temperature, via
the model advection and radiation schemes, can significantly alter the zonal-mean cold-point temperature at the
tropical tropopause shown, for example, by Gettelman
et al. (2010) to be highly correlated to lower stratospheric
water vapor concentrations. Furthermore, the impacts of
these processes on upper-tropospheric water vapor concentrations have a greater influence on lower-stratospheric
water vapor concentrations in climate models than in
the real world, because of the vertical transport of biases
in upper-tropospheric water vapor concentrations necessarily included within the model vertical advection of
6531
moisture. Thus, the uncertainty in upper-tropospheric
water vapor concentrations arising in part from the
model’s various parameterization schemes is particularly
relevant to correctly modeling stratospheric water vapor
concentrations. Of course, with sufficient vertical and
temporal resolution, such vertical smoothing will be minimized (C. Smith et al. 2015, unpublished manuscript).
Accurate modeling of stratospheric water vapor concentrations is essential, as they directly influence surface
climate, the tropospheric jet streams, and the evolution
of stratospheric chemistry. If modeled interactively, the
radiative impacts of greenhouse gases can further increase the uncertainty in stratospheric water vapor.
In general, the processes discussed in this work are
found to have a minimal impact on temperatures and
water vapor concentrations outside the tropopause region (not shown). Of course, in addition to these processes, there are other processes impacting the tropical
tropopause region. For example, the magnitude of
gravity wave fluxes into the stratosphere is poorly constrained and will influence the magnitude of the Brewer–
Dobson circulation. This, in turn, is known to influence
the cold-point temperature (Gettelman et al. 2010).
However, in the MetUM this process was not found to
have a significant impact on the temperature of the
tropical tropopause or on stratospheric water vapor
concentrations, and so has not been discussed in this
work. Another process found to have negligible impact
on tropical tropopause temperatures was the latent heat
of vaporization of ice in this region. Also, the effect of
cirrus clouds on the radiation budget, and therefore the
cold-point temperature, can be influenced by the presence of anthropogenic aerosols through aerosol indirect
effects (Haywood and Boucher 2000; Sherwood 2002;
Gettelman et al. 2012), although these are only considered via cirrus cloud amounts in this study.
The focus of this study has been mainly on annualmean biases. In the MetUM it is found that annual-mean
biases in tropical tropopause temperature and stratospheric water vapor are positive, but the biases in the
magnitude of the seasonal cycle of these quantities are
negative (i.e., the modeled annual cycle is too weak).
Most of the processes discussed in this paper tend to
increase or decrease both, which suggests that there may
be other processes that are still missing from the model
or that further improvement is required to those processes discussed. Therefore, improving both the annualmean biases and the biases in the magnitude of the
seasonal cycle is certainly a subject for further work.
A key aim of this work was to demonstrate how and
why the tropical tropopause is sensitive to many different processes in a climate model. When building an
Earth system model (ESM) for climate simulations, any
6532
JOURNAL OF CLIMATE
one of these processes can produce a bias in the tropical tropopause region large enough to impact the
simulated climate change, and accurate modeling of
each process is therefore important for performing
accurate climate simulations with that ESM. The
tropical tropopause temperature bias and tropical lowerstratospheric water vapor concentrations provide a metric for simultaneously constraining both physical and
Earth systems processes and their feedbacks. This study
has used sensitivity experiments to understand the effects
of individual processes, and it is hoped that the insight
into these processes gained from this study will help other
Earth system modeling groups to more accurately represent both tropical tropopause temperatures and stratospheric water vapor concentrations.
Acknowledgments. The authors thank Steve Derbyshire,
Markus Gross, David Jackson, Adam Scaife, and Alison
Stirling for useful discussions during the early stages of
this work. The work of SCH, NB, FO’C, and KW was
supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). SCH and
NB were supported by the European Community within
the StratoClim project (Grant 603557). MW received
funding by the Australian Government through the
Australian Climate Change Science Programme. We
acknowledge use of the MONSooN high performance
computing system, a collaborative facility supplied
under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council.
ERA-Interim data have been provided by the ECMWF
Data Server. MERRA data have been have been provided by the Global Modeling and Assimilation Office
(GMAO) at NASA Goddard Space Flight Center
through the NASA GES DISC online archive. The
authors thank Greg Bodeker (Bodeker Scientific) and
Birgit Hassler (NOAA) for providing the combined
vertical ozone profile database. The authors acknowledge Sean Davis and Karen Rosenlof for permitting
use of the SWOOSH dataset. The authors thank Mark
Machin for producing Fig. 1. This paper is U.K. Crown
Copyright.
REFERENCES
Baran, A. J., P. Hill, K. Furtado, P. Field, and J. Manners, 2014: A
coupled cloud physics–radiation parameterization of the bulk
optical properties of cirrus and its impact on the Met Office
Unified Model Global Atmosphere 5.0 configuration. J. Climate,
27, 7725–7752, doi:10.1175/JCLI-D-13-00700.1.
Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc.,
92, 1023–1043, doi:10.1175/2011BAMS2856.1.
VOLUME 28
Bodeker Scientific, 2008: The Binary Data Base of Profiles, tier 1.4,
version 1.1.0.6. Bodeker Scientific, accessed 10 February 2014.
[Available online at http://www.bodekerscientific.com/data/
the-bdbp.]
Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and
A. Shelly, 2012: Unified modeling and prediction of weather
and climate: A 25-year journey. Bull. Amer. Meteor. Soc., 93,
1865–1877, doi:10.1175/BAMS-D-12-00018.1.
Butchart, N., 2014: The Brewer–Dobson circulation. Rev. Geophys.,
52, 157–184, doi:10.1002/2013RG000448.
Chepfer, H., S. Bony, D. Winker, M. Chiriaco, J.-L. Dufresne, and
G. Sèze, 2008: Use of CALIPSO lidar observations to evaluate
the cloudiness simulated by a climate model. Geophys. Res.
Lett., 35, L15704, doi:10.1029/2008GL034207.
Ciais, P., and Coauthors, 2013: Carbon and other biogeochemical cycles. Climate Change 2013: The Physical
Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 465–570.
Cionni, I., and Coauthors, 2011: Ozone database in support of
CMIP5 simulations: Results and corresponding radiative
forcing. Atmos. Chem. Phys., 11, 11 267–11 292, doi:10.5194/
acp-11-11267-2011.
Collins, M., S. F. B. Tett, and C. Cooper, 2001: The internal climate
variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Climate Dyn., 17, 61–81,
doi:10.1007/s003820000094.
Cotton, R. J., and Coauthors, 2013: The effective density of small
ice particles obtained from in situ aircraft observations of midlatitude cirrus. Quart. J. Roy. Meteor. Soc., 139, 1923–1934,
doi:10.1002/qj.2058.
Cullen, M. J. P., 1993: The Unified Forecast/Climate Model. Meteor.
Mag., 122, 81–94.
Cusack, S., J. M. Edwards, and J. M. Crowther, 1999: Investigating
k distribution methods for parameterizing gaseous absorption
in the Hadley Centre Climate Model. J. Geophys. Res., 104
(D2), 2051–2057, doi:10.1029/1998JD200063.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis:
Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/
qj.828.
Derbyshire, S. H., A. V. Maidens, S. F. Milton, R. A. Stratton, and
M. R. Willett, 2011: Adaptive detrainment in a convective
parametrization. Quart. J. Roy. Meteor. Soc., 137, 1856–1871,
doi:10.1002/qj.875.
Dessler, A. E., M. R. Schoeberl, T. Wang, S. M. Davis, K. H.
Rosenlof, and J.-P. Vernier, 2014: Variations of stratospheric water vapor over the past three decades.
J. Geophys. Res. Atmos., 119, 12 588–12 598, doi:10.1002/
2014JD021712.
Dinh, T., and S. Fueglistaler, 2014: Microphysical, radiative, and
dynamical impacts of thin cirrus clouds on humidity in the
tropical tropopause layer and lower stratosphere. Geophys.
Res. Lett., 41, 6949–6955, doi:10.1002/2014GL061289.
ECMWF, 2011: ERA-Interim dataset (January 1979 to present).
ECMWF Data Server, accessed 20 October 2014. [Available
online at http://apps.ecmwf.int/datasets/data/interim-fullmoda/?levtype5pl.]
Edwards, J. M., and A. Slingo, 1996: Studies with a flexible new
radiation code. I: Choosing a configuration for a large-scale
model. Quart. J. Roy. Meteor. Soc., 122, 689–719, doi:10.1002/
qj.49712253107.
——, S. Havemann, J.-C. Thelen, and A. J. Baran, 2007: A new
parametrization for the radiative properties of ice crystals:
15 AUGUST 2015
HARDIMAN ET AL.
Comparison with existing schemes and impact in a GCM.
Atmos. Res., 83, 19–35, doi:10.1016/j.atmosres.2006.03.002.
Evan, S., K. H. Rosenlof, J. Dudhia, B. Hassler, and S. M. Davis,
2013: The representation of the TTL in a tropical channel
version of the WRF model. J. Geophys. Res. Atmos., 118,
2835–2848, doi:10.1002/jgrd.50288.
Field, P. R., A. J. Heymsfield, and A. Bansemer, 2006: Shattering
and particle interarrival times measured by optical array
probes in ice clouds. J. Atmos. Oceanic Technol., 23, 1357–
1371, doi:10.1175/JTECH1922.1.
——, ——, and ——, 2007: Snow size distribution parameterization
for midlatitude and tropical ice clouds. J. Atmos. Sci., 64, 4346–
4365, doi:10.1175/2007JAS2344.1.
Flannaghan, T. J., and S. Fueglistaler, 2011: Kelvin waves and
shear-flow turbulent mixing in the TTL in (re-)analysis data.
Geophys. Res. Lett., 38, L02801, doi:10.1029/2010GL045524.
——, and ——, 2014: Vertical mixing and the temperature and
wind structure of the tropical tropopause layer. J. Atmos. Sci.,
71, 1609–1622, doi:10.1175/JAS-D-13-0321.1.
Flato, G., and Coauthors, 2013: Evaluation of climate models.
Climate Change 2013: The Physical Science Basis, T. F. Stocker
et al., Eds., Cambridge University Press, 741–866.
Forster, P. M. de F., and K. P. Shine, 1999: Stratospheric water vapour changes as a possible contributor to observed stratospheric cooling. Geophys. Res. Lett., 26, 3309–3312, doi:10.1029/
1999GL010487.
——, and ——, 2002: Assessing the climate impact of trends in
stratospheric water vapor. Geophys. Res. Lett., 29 (6), doi:10.1029/
2001GL013909.
Fueglistaler, S., and P. H. Haynes, 2005: Control of interannual and
longer-term variability of stratospheric water vapor. J. Geophys.
Res., 110, D24108, doi:10.1029/2005JD006019.
——, A. E. Dessler, T. J. Dunkerton, I. Folkins, Q. Fu, and P. W.
Mote, 2009: Tropical tropopause layer. Rev. Geophys., 47,
RG1004, doi:10.1029/2008RG000267.
——, and Coauthors, 2013: The relation between atmospheric
humidity and temperature trends for stratospheric water.
J. Geophys. Res. Atmos., 118, 1052–1074, doi:10.1002/
jgrd.50157.
Furtado, K., P. R. Field, R. Cotton, and A. J. Baran, 2015: The
sensitivity of simulated high cloud to ice crystal fall speed,
shape and size distribution. Quart. J. Roy. Meteor. Soc.,
doi:10.1002/qj.2457, in press.
Gettelman, A., and Coauthors, 2010: Multimodel assessment of
the upper troposphere and lower stratosphere: Tropics and
global trends. J. Geophys. Res., 115, D00M08, doi:10.1029/
2009JD013638.
——, X. Liu, D. Barahona, U. Lohmann, and C. Chen, 2012: Climate impacts of ice nucleation. J. Geophys. Res., 117, D20201,
doi:10.1029/2012JD017950.
Global Modeling and Assimilation Office, 2011: Modern-Era
Retrospective Analysis for Research and Applications. Goddard Earth Sciences Data and Information Services Center,
accessed 15 September 2014. [Available online at http://disc.
sci.gsfc.nasa.gov/daac-bin/DataHoldings.pl.]
Goff, J. A., 1965: Saturation pressure of water on the new Kelvin
scale. Humidity and Moisture: Fundamentals and Standards,
A. Wexler and W. A. Wildhack, Eds., 289 pp.
Gregory, D., and P. R. Rowntree, 1990: A mass flux convection
scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev.,
118, 1483–1506, doi:10.1175/1520-0493(1990)118,1483:
AMFCSW.2.0.CO;2.
6533
——, and S. Allen, 1991: The effect of convective scale downdrafts upon NWP and climate simulations. Ninth Conf. on
Numerical Weather Prediction. Denver, CO, Amer. Meteor.
Soc., 122–123.
Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization
of the global radiosonde temperature dataset through combined comparison with reanalysis background series and
neighboring stations. J. Climate, 25, 8108–8131, doi:10.1175/
JCLI-D-11-00668.1.
——, and Coauthors, 2013: Radiosonde Innovation Composite Homogenization (RICH) dataset, version 1.5. Institut fur Meteorologie and Geophysik, accessed 13 April 2015. [Available online
at http://www.univie.ac.at/theoret-met/research/raobcore/.]
Hardiman, S. C., N. Butchart, S. M. Osprey, L. J. Gray, A. C.
Bushell, and T. J. Hinton, 2010: The climatology of the middle
atmosphere in a vertically extended version of the Met Office’s climate model. Part I: Mean state. J. Atmos. Sci., 67,
1509–1525, doi:10.1175/2009JAS3337.1.
——, ——, and N. Calvo, 2014: The morphology of the Brewer–
Dobson circulation and its response to climate change in
CMIP5 simulations. Quart. J. Roy. Meteor. Soc., 140, 1958–
1965, doi:10.1002/qj.2258.
Hassler, B., G. E. Bodeker, and M. Dameris, 2008: Technical
note: A new global database of trace gases and aerosols
from multiple sources of high vertical resolution measurements. Atmos. Chem. Phys., 8, 5403–5421, doi:10.5194/
acp-8-5403-2008.
——, ——, I. Cionni, and M. Dameris, 2009: A vertically resolved,
monthly mean, ozone database from 1979 to 2100 for constraining global climate model simulations. Int. J. Remote
Sens., 30, 4009–4018, doi:10.1080/01431160902821874.
Haywood, J., and O. Boucher, 2000: Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review.
Rev. Geophys., 38, 513–543, doi:10.1029/1999RG000078.
Hegglin, M. I., and T. G. Shepherd, 2009: Large climate-induced
changes in ultraviolet index and stratosphere-to-troposphere
ozone flux. Nat. Geosci., 2, 687–691, doi:10.1038/ngeo604.
——, and Coauthors, 2013: SPARC data initiative: Comparison of
water vapor climatologies from international satellite limb
sounders. J. Geophys. Res. Atmos., 118, 11 824–11 846, doi:10.1002/
jgrd.50752.
Holton, J. R., and A. Gettelman, 2001: Horizontal transport and
the dehydration of the stratosphere. Geophys. Res. Lett., 28,
2799–2802, doi:10.1029/2001GL013148.
Jensen, E. J., and L. Pfister, 2004: Transport and freeze-drying in
the tropical tropopause layer. J. Geophys. Res., 109, D02207,
doi:10.1029/2003JD004022.
——, A. S. Ackerman, and J. A. Smith, 2007: Can overshooting
convection dehydrate the tropical tropopause layer? J. Geophys.
Res., 112, D11209, doi:10.1029/2006JD007943.
Jones, C. D., and Coauthors, 2011: The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model
Dev., 4, 543–570, doi:10.5194/gmd-4-543-2011.
Kim, J., K. M. Grise, and S.-W. Son, 2013: Thermal characteristics of the cold-point tropopause region in CMIP5 models.
J. Geophys. Res. Atmos., 118, 8827–8841, doi:10.1002/jgrd.50649.
Kirk-Davidoff, D. B., E. J. Hintsa, J. G. Anderson, and D. W.
Keith, 1999: The effect of climate change on ozone depletion
through changes in stratospheric water vapour. Nature, 402,
399–401, doi:10.1038/46521.
Korolev, A. V., and G. A. Isaac, 2005: Shattering during sampling
by OAPs and HVPS. Part 1: Snow particles. J. Atmos. Oceanic
Technol., 22, 528–542, doi:10.1175/JTECH1720.1.
6534
JOURNAL OF CLIMATE
Lacis, A., D. Wuebbles, and J. Logan, 1990: Radiative forcing of
climate by changes in the vertical distribution of ozone.
J. Geophys. Res., 95, 9971–9981, doi:10.1029/JD095iD07p09971.
Madronich, S., R. L. McKenzie, and M. M. Caldwell, 1995: Changes
in ultraviolet radiation reaching the earth’s surface. Ambio,
24, 143–152.
Martin, G. M., M. A. Ringer, V. D. Pope, A. Jones, C. Dearden, and
T. J. Hinton, 2006: The physical properties of the atmosphere in
the new Hadley Centre Global Environmental Model
(HadGEM1). Part I: Model description and global climatology.
J. Climate, 19, 1274–1301, doi:10.1175/JCLI3636.1.
——, and Coauthors, 2011: The HadGEM2 family of Met Office
Unified Model climate configurations. Geosci. Model Dev., 4,
723–757, doi:10.5194/gmd-4-723-2011.
Maycock, A. C., M. M. Joshi, K. P. Shine, and A. A. Scaife, 2013:
The circulation response to idealized changes in stratospheric water vapor. J. Climate, 26, 545–561, doi:10.1175/
JCLI-D-12-00155.1.
——, ——, ——, S. M. Davis, and K. H. Rosenlof, 2014: The
potential impact of changes in lower stratospheric water
vapour on stratospheric temperatures over the past 30 years.
Quart. J. Roy. Meteor. Soc., 140, 2176–2185, doi:10.1002/
qj.2287.
McKenzie, R., B. Connor, and G. Bodeker, 1999: Increased
summertime UV radiation in New Zealand in response to
ozone loss. Science, 285, 1709–1711, doi:10.1126/
science.285.5434.1709.
Morcrette, C. J., 2012: Improvements to a prognostic cloud scheme
through changes to its cloud erosion parametrization. Atmos.
Sci. Lett., 13, 95–102, doi:10.1002/asl.374.
Morgenstern, O., P. Braesicke, F. M. O’Connor, A. C. Bushell,
C. E. Johnson, S. M. Osprey, and J. A. Pyle, 2009: Evaluation
of the new UKCA climate-composition model—Part 1: The
stratosphere. Geosci. Model Dev., 2, 43–57, doi:10.5194/
gmd-2-43-2009.
Mote, P. W., and Coauthors, 1996: An atmospheric tape recorder:
The imprint of tropical tropopause temperatures on stratospheric water vapor. J. Geophys. Res., 101 (D2), 3989–4006,
doi:10.1029/95JD03422.
NOAA/Earth Systems Research Laboratory, 2014: Stratospheric
Water and Ozone Satellite Homogenized dataset, version
02.1. NOAA/Earth System Research Laboratory Chemical
Sciences Division, accessed 29 April 2014. [Available online at
http://www.esrl.noaa.gov/csd/groups/csd8/swoosh/.]
Nowack, P. J., N. L. Abraham, A. C. Maycock, P. Braesicke, J. M.
Gregory, M. M. Joshi, A. Osprey, and J. A. Pyle, 2015: A large
ozone-circulation feedback and its implications for global
warming assessments. Nat. Climate Change, 5, 41–45, doi:10.1038/
nclimate2451.
O’Connor, F. M., and Coauthors, 2014: Evaluation of the new
UKCA climate-composition model—Part 2: The troposphere.
Geosci. Model Dev., 7, 41–91, doi:10.5194/gmd-7-41-2014.
Orbe, C., M. Holzer, and L. M. Polvani, 2012: Flux distributions
as robust diagnostics of stratosphere–troposphere exchange. J. Geophys. Res., 117, D01302, doi:10.1029/
2011JD016455.
Pope, V. D., M. L. Gallani, P. R. Rowntree, and R. A. Stratton,
2000: The impact of new physical parametrizations in the
Hadley Centre climate model—HadAM3. Climate Dyn., 16,
123–146, doi:10.1007/s003820050009.
Priestley, A., 1993: A quasi-conservative version of the semiLagrangian advection scheme. Mon. Wea. Rev., 121, 621–629,
doi:10.1175/1520-0493(1993)121,0621:AQCVOT.2.0.CO;2.
VOLUME 28
Rienecker, M. M., and Coauthors, 2011: MERRA: NASAs
Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/
JCLI-D-11-00015.1.
Roscoe, H. K., and J. D. Haigh, 2007: Influences of ozone depletion, the solar cycle and the QBO on the southern annular
mode. Quart. J. Roy. Meteor. Soc., 133, 1855–1864,
doi:10.1002/qj.153.
Rosenlof, K. H., and Coauthors, 2001: Stratospheric water vapor
increases over the past half-century. Geophys. Res. Lett., 28,
1195–1198, doi:10.1029/2000GL012502.
Rothman, L. S., and Coauthors, 2013: The HITRAN2012 molecular spectroscopic database. J. Quant. Spectrosc. Radiative
Transfer, 130, 4–50, doi:10.1016/j.jqsrt.2013.07.002.
Russell, J. M., III, and Coauthors, 1993: The Halogen Occultation
Experiment. J. Geophys. Res., 98 (D6), 10 777–10 797, doi:10.1029/
93JD00799.
Seiki, T., C. Kodama, A. T. Noda, and M. Satoh, 2015: Improvements in global cloud-system resolving simulations by using a
double-moment bulk cloud microphysics scheme. J. Climate,
28, 2405–2419, doi:10.1175/JCLI-D-14-00241.1.
Sherwood, S. C., 2002: A microphysical connection among biomass
burning, cumulus clouds, and stratospheric moisture. Science,
295, 1272–1275, doi:10.1126/science.1065080.
Solomon, S., R. R. Garcia, F. S. Rowland, and D. J. Wuebbles,
1986: On the depletion of Antarctic ozone. Nature, 321, 755–
758, doi:10.1038/321755a0.
——, K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis,
T. J. Sanford, and G.-K. Plattner, 2010: Contributions of
stratospheric water vapor to decadal changes in the rate of
global warming. Science, 327, 1219–1223, doi:10.1126/
science.1182488.
Son, S.-W., and Coauthors, 2008: The impact of stratospheric ozone
recovery on the Southern Hemisphere westerly jet. Science,
320, 1486–1489, doi:10.1126/science.1155939.
Staniforth, A., and J. C^
oté, 1991: Semi-Lagrangian integration
schemes for atmospheric models—A review. Mon. Wea.
Rev., 119, 2206–2223, doi:10.1175/1520-0493(1991)119,2206:
SLISFA.2.0.CO;2.
Stenke, A., V. Grewe, and M. Ponater, 2008: Lagrangian transport of
water vapor and cloud water in the ECHAM4 GCM and its
impact on the cold bias. Climate Dyn., 31, 491–506, doi:10.1007/
s00382-007-0347-5.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of
CMIP5 and the experiment design. Bull. Amer. Meteor. Soc.,
93, 485–498, doi:10.1175/BAMS-D-11-00094.1.
Telford, P. J., and Coauthors, 2013: Implementation of the Fast-JX
Photolysis scheme (v6.4) into the UKCA component of the
MetUM chemistry-climate model (v7.3). Geosci. Model Dev.,
6, 161–177, doi:10.5194/gmd-6-161-2013.
Toon, O. B., R. P. Turco, J. Jordan, J. Goodman, and G. Ferry,
1989: Physical processes in polar stratospheric ice clouds.
J. Geophys. Res., 94, 11 359–11 380, doi:10.1029/
JD094iD09p11359.
Walters, D. N., and Coauthors, 2011: The Met Office Unified
Model Global Atmosphere 3.0/3.1 and JULES Global Land
3.0/3.1 configurations. Geosci. Model Dev., 4, 919–941,
doi:10.5194/gmd-4-919-2011.
——, and Coauthors, 2014: The Met Office Unified Model Global
Atmosphere 4.0 and JULES Global Land 4.0 configurations.
Geosci. Model Dev., 7, 361–386, doi:10.5194/gmd-7-361-2014.
Waters, J. W., and Coauthors, 2006: The Earth Observing System
Microwave Limb Sounder (EOS MLS) on the Aura satellite.
15 AUGUST 2015
HARDIMAN ET AL.
IEEE Trans. Geosci. Remote Sens., 44, 1075–1092, doi:10.1109/
TGRS.2006.873771.
Williamson, D. L., 1990: Semi-Lagrangian moisture transport in
the NMC spectral model. Tellus, 42A, 413–428, doi:10.1034/
j.1600-0870.1990.t01-3-00002.x.
Wilson, D. R., and S. P. Ballard, 1999: A microphysically based precipitation scheme for the UK meteorological office unified
model. Quart. J. Roy. Meteor. Soc., 125, 1607–1636, doi:10.1002/
qj.49712555707.
——, A. C. Bushell, A. M. Kerr-Munslow, J. D. Price, and C. J.
Morcrette, 2008: PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description. Quart. J. Roy. Meteor. Soc.,
134, 2093–2107, doi:10.1002/qj.333.
6535
WMO, 1957: A three-dimensional science: Second session of the
commission for aerology. WMO Bull., 6, 134–138.
Wood, N., and Coauthors, 2014: An inherently mass-conserving semiimplicit semi-Lagrangian discretization of the deep-atmosphere
global non-hydrostatic equations. Quart. J. Roy. Meteor. Soc.,
140, 1505–1520, doi:10.1002/qj.2235.
Zahn, A., E. Christner, P. F. J. van Velthoven, A. RautheSchch, and C. A. M. Brenninkmeijer, 2014: Processes
controlling water vapor in the upper troposphere/
lowermost stratosphere: An analysis of 8 years of monthly
measurements by the IAGOS-CARIBIC observatory.
J. Geophys. Res. Atmos., 119, 11 505–11 525, doi:10.1002/
2014JD021687.